A Computational Investigation of Maximizing Matching in Double-sided Auctions

Jinzhong Niu and Simon Parsons. A Computational Investigation of Maximizing Matching in Double-sided Auctions. Journal of Autonomous Agents and Multiagent Systems, under review.

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Abstract

In this paper, we introduce a novel, non-recursive, maximal matching algorithm for double auctions, which aims to maximize the amount of commodities to be traded. It differs from the usual equilibrium matching, which clears a market at the equilibrium price. We compare the two algorithms through experimental analyses, showing that the maximal matching algorithm is favored in scenarios where trading volume is a priority and that it may possibly improve allocative efficiency over equilibrium matching as well. A parameterized algorithm that incorporates both maximal matching and equilibrium matching as special cases is also presented to allow flexible control on how much to trade in a double auction.